Examining Risky Riding Behaviors: Insights from a Questionnaire Survey with Middle-Aged and Older Motorcyclists in Thailand

: This research endeavors to achieve three primary objectives. Firstly, it seeks to develop a measurement model capable of assessing the motorcycle riding behavior of middle-aged individuals in Thailand. The construction of this model integrates the motorcycle rider behavior questionnaire (MRBQ) with statistical and descriptive analyses. Secondly, the research examines the accuracy of the measurement model using a factor analysis approach, comprising both exploratory and confirmatory factor analyses. Lastly, the study aims to furnish the people of Thailand with a set of guidelines for self-assessment of their motorcycle riding habits. The examination involves 399 middle-aged motorcycle riders aged 35 years or older, a significant majority of whom (81%) possess the requisite licenses for motorcycle operation, with the majority (83%) accumulating over a decade of riding experience. Through analysis, a set of 39 items capturing nuanced behaviors exhibited by middle-aged motorcyclists in Thailand is identified. These items are categorized into four distinct clusters: speed violations, control errors, traffic errors, and adherence to safety equipment protocols. The utilization of the MRBQ in this study holds significant importance, as it provides valuable insights into the riding practices of the Thai population. The resultant findings hold the potential to inform safety initiatives and strategies aimed at enhancing the overall motorcycle riding experience.


Introduction
Worldwide, road traffic accidents are the cause of death of about 1.2 million people every year, making it one of the leading causes of death.Moreover, the World Health Organization also showed that motorcycle accidents in South-East Asia account for 43% of those road traffic accident deaths, and 74% of motorized accidents occur in Thailand [1].Otherwise, people across the globe are enjoying longer lifespans, with the majority of individuals now living into their sixties and beyond.The older population is growing both in numbers and as a proportion of the total population in every country.By 2030, roughly one in six people worldwide will be aged 60 or older.The number of people in this age group is set to grow from a billion in 2020 to 2.1 billion by 2050.The population of those aged 80 and older is also predicted to triple, reaching 426 million between 2020 and 2050.While the trend of population aging initially started in countries with high incomes, such as Japan where over 30% of the population is already over 60, now it is the low-and middle-income countries that are experiencing the most significant changes.By 2050, around two-thirds of the world's population aged 60 and above will be living in these lower-and middle-income countries [1].
From the records of the Police Information System Center, the Royal Thai Police indicates there was an increasing trend of accidents from 2011 to 2022 of approximately 36.25%.For the economic losses due to road accidents, persons injured by road accidents increased approximately by 152.18%.Moreover, motorcycles have been the most popular vehicles among Thai people over a long period; the statistics from 2020 show that approximately 19.40% of accidents involved motorcycles, compared with passenger cars being involved in 15.67%, and light trucks being involved (pick-ups) in 9.20%.Evans et al. [2] found that humans influence 95 percent of accidents, while the road environment and vehicle-related factors influence 28 percent and 8 percent of accidents.It might be suggested that traffic accidents resemble a severe and chronic disease that occurs every day and whose threat to health, quality of life, and people's property is significant.Therefore, this is an urgent problem that everyone and every department must take responsibility for and pay considered attention to working together to solve these problems and prevent accidents.However, in order to prevent accidents, we must first be aware of the cause of accidents.
Risky behaviors while riding a motorcycle can lead to accidents on the road [3].To reduce these accidents, it is crucial to understand the behaviors that put motorcyclists at risk.That is where the motorcycle rider behavior questionnaire becomes involved, developed by Elliot and colleagues [4].They took inspiration from the driver behavior questionnaire (DBQ), a well-known tool used to study how truck and car drivers behave on the road, including their involvement in accidents [5].Research has shown that people who score higher on the DBQ are more likely to have been in accidents in the past and are also at a higher risk of future accidents [6].Thus, in addition to car drivers and motorcycle users, there is the WBQ version for pedestrians, the CBQ for cyclists, among others (e.g., Useche et al. [7]).This demonstrates the validity of the questionnaire and its effective applicability for different users and cultural contexts.Building on the success of the driver behavior questionnaire, Elliot and his team created the MRBQ specifically for motorcycle behaviors.The original MRBQ examines five aspects: mistakes in traffic unintentional errors by the rider; problems with controlling the motorcycle; speeding; performing stunts intentionally for excitement; and the use of safety gear.Over time, different ways of looking at the MRBQ were suggested, and extra questions were added to the questionnaire.The varying results from MRBQ studies show that road safety differs significantly between countries.
All of this discussion points to the need for studying how motorcycles are used in Thailand.Previous research has used MRBQ in various locations and with different groups of people.So, collecting and analyzing data from surveys to understand risky riding behaviors, especially on motorcycles, is currently of real importance.This type of data could help with forward planning, prevention of accidents, and lowering the chances and seriousness of severe injuries if accidents do happen-like fractures or dislocations, where organs can be injured, causing a lot of pain and serious harm.From the world-wide reports that the rates of accidents for people in low-and middle-income countries are increasing, likewise, The 5th Road Safety Master Plan of Thailand (2022-2027) places a strong emphasis on addressing significant risks and threats to the country's security in a serious and timely manner [8].It addresses concerns related to all road users, with a particular focus on high-risk groups such as middle-aged and older individuals.The MRBQ was used as a research tool to collect data from middle-aged motorcyclists in Thailand.Statisticians and factor analysis techniques use variables to identify unsafe driving behavior in Thailand.The findings from this study can contribute to enhancing, rectifying, or designing strategies aimed at effectively preventing accidents and minimizing losses from accidents in the future.

Risk and Older Motorcyclists
The growing number of older people in the world requires a thorough investigation of the practices of senior riders, including how they engage with different forms of transportation.The issue of safety holds significant importance, as scholarly studies have demonstrated that older individuals are more susceptible to accidents and injuries due to their physical fragility and reduced sensory capabilities.This vulnerability is particularly evident in the context of driving, where older adults face heightened risks at intersections and during lane changes.These risks can be primarily attributed to cognitive decline and a decrease in reaction times [9].In addition to safety considerations, it is imperative to comprehend the travel preferences of older adults, as they demonstrate unique tendencies towards various modes of transportation.Convenience, cost, and accessibility are some of the factors that influence these inclinations, which ultimately determine whether people use public transportation, continue to rely on their personal vehicles, or ask family members for help [10].Furthermore, the various obstacles faced by older adults, including diminished physical mobility resulting from health conditions, financial limitations, and hurdles in utilizing public transportation services, exert a substantial impact on their decision-making process regarding transportation options [11].It is imperative to acknowledge and tackle these obstacles in order to encourage increased engagement in riding activities among the older population.This necessitates the implementation of customized interventions, including driver training programs that are suitable for their age group, enhancements in public transportation services, and the development of a pedestrian and cycling infrastructure that accommodates seniors [12].
The study of older riding behaviors is of paramount importance in light of the aging global population.Safety remains a central concern, with older adults facing increased vulnerability to accidents, particularly during driving, attributed to cognitive decline and reduced reaction times [13].Understanding their travel preferences, which are influenced by convenience, cost, and accessibility, is crucial for designing inclusive and age-friendly transportation systems [14].Addressing the challenges faced by the older population, including reduced mobility due to health issues and financial constraints, is essential to promote active riding behaviors [15].Tailored interventions, such as age-appropriate driver training programs, improved public transit services, and a senior-friendly infrastructure, hold the potential to enhance the mobility and overall quality of life for older adults [16].The body of research in this field continues to evolve, and ongoing efforts are required to adapt transportation systems to meet the specific needs and preferences of the older population, ultimately ensuring their safe and efficient mobility in an aging society.

Motorcycle Rider Behavior Questionnaire
According to earlier studies conducted by Vlahogianni et al. [17], there are a few different techniques to evaluate rider behavior and how it relates to accidents.In situations where there is a limited availability of resources for direct observation and official traffic records, the utilization of the motorcycle rider behavior questionnaire assumes a critical role.The present questionnaire provides significant insights into the behavioral patterns exhibited by individuals during motorcycle riding in various traffic scenarios, rendering it an invaluable instrument for both motorcycle safety research and practical application.The incorporation of stunt performance in the MRBQ has demonstrated a notable correlation with accidents among motorcyclists in Australia [18].According to [19], this particular feature has also played a substantial role in actual accidents and instances of traffic violations among riders in Turkey.Although safety equipment was frequently evaluated, its impact on the occurrence of accidents was not significant.The paramount significance lies in guaranteeing the reliability and validity of the MRBQ when evaluating motorcycle safety initiatives with the aim of comprehending and mitigating accidents.Numerous scholarly investigations have extended beyond the conventional MRBQ methodology.For example, the authors of [20] conducted a study in which they examined individuals who had a minimum of three years of experience in motorcycle riding.
Researchers [21] have utilized the MRBQ to evaluate socio-demographic factors and the frequency of accidents among the participants.A factor structure was constructed that exhibits alignment with the structures of the MRBQ in other nations.While earlier studies primarily concentrated on industrialized nations such as Australia, Slovenia, and the UK, subsequent studies have extended the utilization of the MRBQ to emerging countries like Vietnam.Nevertheless, the outcomes of these inquiries exhibit variability, underscoring the imperative to reassess and validate the efficacy of the MRBQ in various foreign settings [22].It is clear that the MRBQ is an essential instrument for comprehending riding behavior and how it relates to accidents, especially in situations where resources are limited.Recent studies have shown the correlation between stunt performances and accident engagement across different nations, while also shedding light on the relatively minimal influence of safety equipment.In order to uphold the effectiveness of motorcycle safety initiatives, it is crucial to sustain the reliability and validity of the MRBQ.The research related work shown in Table 1.

Analysis Methods in Behavior Assessment
A popular statistical technique for testing research hypotheses by incorporating pertinent theories or concepts is structural equation modeling (SEM) [25][26][27].A recent study utilized SEM to investigate the personality traits, socio-cognitive features, and risky riding behaviors in a sample of 1028 inexperienced Italian drivers.The findings of the study indicate that there is an indirect influence of personality on riding behavior, mediated by risk perception, in addition to a direct influence.The relationship between riding behavior, social norms, and personality was found to be substantially influenced by risk perception.Both genders can provide evidence for this claim [28].Furthermore, the study conducted by Mokarami et al. [29] employed the Mokarami method to examine the associations among latent factors pertaining to safety culture, unsafe behavior (acting as a mediator), and accidents.This approach elucidated the causes of accidents by utilizing observable variables.Typically, researchers employ descriptive measurements and goodness-of-fit statistical indices to evaluate the degree of alignment between the model and the observed data.
In order to better understand rider behavior, researchers in this discipline have also employed a variety of statistical techniques.Significantly, researchers have utilized exploratory factor analysis (EFA) as a methodology to discern latent components that contribute to the manifestation of hazardous riding behaviors.Stanojevic et al. [30] employed EFA as a methodological approach to investigate the psychological determinants that underlie risk-taking behaviors among motorcyclists.Their study identified specific components such as sensation-seeking and impulsivity as significant contributors to this phenomenon.Furthermore, within the realm of cycling helmet usage, a study conducted by [31] employed this analysis method to uncover factors that influence helmet-wearing behavior.The study identified variables such as safety attitudes and peer influences, shedding light on the intricate dynamics involved in this behavior.Moreover, confirmatory factor analysis (CFA) has been important in the validation of models and measurement devices employed in the investigation of rider behavior.Moreover, a previous study utilized confirmatory factor analysis as a methodological approach to establish the reliability and validity of a motorcycle rider behavior scale [32].This scale serves as a crucial instrument for evaluating rider behaviors and attitudes.Furthermore, another research study used it to validate the suggested factor framework in the area of risk perception among car drivers, showing how important factors like risk acceptance and tolerance are in shaping driver behavior [33].Finally, the utilization of analysis of variance has been crucial in examining the influence of external factors on rider behavior.The researcher carried out an investigation to look into the influence of age on motorcycle riders' behavior.The results indicated noteworthy disparities between younger and older riders, with younger riders demonstrating a propensity for engaging in more hazardous activities [34].In a similar vein, a recent study utilized this method to examine gender discrepancies in the behavior of individuals who engage in bicycle riding [35].The researchers emphasized noteworthy disparities in terms of compliance with rules and the occurrence of accidents among male and female riders.

Sample Size and Participants
This study focuses on the risky riding habits of Thai motorcycle riders who are middleaged and older, considered aged 35 to over 64 years old [36][37][38][39][40].The participants, who were randomly selected from Thailand's motorcycle riding population, were all above 35 years old, capable of riding motorcycles, and either in possession of or without a riding license.The required sample size for analysis comprised a viable sample size greater than five to fifteen times the observed variables [41].Given that there were 39 items in the survey, between 195 and 585 samples were required.Nonetheless, the replies were gathered from 399 individuals in order to enhance the current research quality.Ultimately, after deleting 21 incomplete survey forms, 399 of the 420 responses were included in the final analysis.

Data Collection and Survey Instrument
The surveys were conducted utilizing the pre-designed questions from 1 December 2022 to 20 February 2023.The survey was distributed using an online Google Form through various social media platforms like Facebook, Line, and X.This was conducted through random sampling, reaching people from all across Thailand.The form of the questionnaire was created in accordance with motorcycle riding behaviors by reviewing the relevant literature and studying conceptual ideas and associated work that is shown in Appendix A, Table A1; meanwhile, the validity was then confirmed by experts.The survey instrument employed in this study was partitioned into halves.The initial segment comprised sociodemographic information of the participants, such as their gender, age, educational attainment, occupation, driving license type, frequency of motorcycle usage, riding experience, and traffic knowledge.Respondents were then requested to assess their own safety conduct.The second section of the questionnaire comprised the adapted MRBQ, which includes 24 items from the original questionnaire [4] and 15 extra items that pertain to risky riding behavior seen in Thai motorcyclists.On a 6-point scale, respondents were requested to indicate the frequency with which they engaged in risky driving practices over the last year.A rating of 1 indicates never, 2 indicates rarely, 3 indicates often, 4 indicates rather often, 5 indicates frequently, and 6 indicates always.

Data Analysis
First, we examined the participants' basic information using percentages to understand their demographics.Then, we used IBM SPSS Statistics version 26 to analyze the data relating to risky riding behaviors.Our analysis involved various approaches, including calculating average scores and assessing the reliability of the results.To ensure reliability, we applied a statistical measure called Cronbach's alpha coefficient, considering values above 0.60 as a reliable ratio [42][43][44].Next, we used exploratory factor analysis (EFA) to discover any patterns in participants' riding behaviors.For EFA, we set certain criteria: Kaiser-Meyer-Olkin (KMO) values needed to be above 0.70, and factor loadings had to surpass 0.50 to be considered significant.To validate the quality of our model, CFA, using AMOS version 21.0 applications, was employed.This technique utilized a refined method known as maximum likelihood estimation.
To measure the good fit of our model aligned with the data, we employed several metrics: chi-square (χ 2 ), degrees of freedom (df) ratio, comparative fit index (CFI), Tucker-Lewis index (TLI), residual mean squared error of approximation (RMSEA), and standardized root mean square residual (SRMR).A model was considered satisfactory if the CFI and TLI values exceeded 0.90 and if the RMSEA and SRMR values were below 0.07 or very close to it.We applied these criteria to assess model fit, drawing inspiration from the work of Browne and Cudeck [45].
Moreover, composite reliability (CR) and average variance extracted (AVE) are commonly used in the analysis of survey data for the reliability and convergent validity of a measurement instrument [46].This can be achieved by calculating Cronbach's alpha coefficient, which represents the extent to which the items in a construct consistently measure the same underlying concept [47].This measurement considers the threshold greater than 0.50 to be significant.The following equation was used to compute the CR values of each latent variable [46]: where FL i denotes the standardized factor loadings of measurement item i, n denotes the number of items within a factor, and ME i refers to error in measurement.
The average variance extracted is a measure used to assess how much of the variability in a construct is accounted for by the construct itself compared to the variability caused by measurement errors.The following equation could be used to compute the AVE value [46]: where FL i denotes the standardized factor loadings of the measurement item i, and n represents the number of items within a factor.

Demographics of Participants
In this study, background demographic information was obtained for the 399 participants who took part in the questionnaire survey.The results are shown in Tables 2 and A2.Most of the participants were male, outnumbering females.The majority fell within the 45-54 age bracket.Among the respondents, 44.6% held a motorcycle riding license.Within the group with licenses, almost all of the respondents had more than 10 years of riding experience.About 52.9% of respondents had a moderate level of traffic knowledge.In terms of education, around 65.4% of participants held a bachelor's degree.

Empirical Analysis
Table 3 shows the crosstab analysis between age groups and riding behavior from all of the 399 respondents, composed of four parts including the area in which motorcycles were used, the motorcycles' engine size, riding times, and riding hours.As can be seen from the table, almost everyone used their motorcycles in the urban area, used a motorcycle size of about 100-150 cc., rode during the daytime, and usually used their motorcycle for under 6 h per day.Also, people aged around 45-54 years old had the highest frequency of usage among all of the groups, followed by the group aged 35-44 years old.
In addition, the Pearson chi-square of people's age association between variable contents was ages and area of using motorcycles (χ 2 = 16.920,df = 9, p > 0.05); ages and engine size of motorcycles (χ 2 = 4.175, df = 9, p > 0.05); ages and riding times (χ 2 = 7.375, df = 3, p > 0.05); and ages and riding hours (χ 2 = 13.625, df = 9, p > 0.05).Following the results obtained, there are no significant differences under the five percent error condition between the observed and the expected frequencies, by ages [49].Thus, there is no correlation between the respondents' age and riding behavior.

Motorcyclist Characteristics on Risk Contexts
The group of ages was analyzed by riding behavior that comprised traffic violations, accidents, and level of injuries using an ANOVA test, as shown in Table 4.The results show that statistically significant differences (p < 0.05) were found between the age group and level of injury, with people who were 35-44 years old showing a mean and standard deviation of 1.72 ± 1.042; thus, they showed a higher chance of having more injuries than the other groups, followed by the group aged over 64 years old (1.71 ± 1.091).However, the statistics illustrated that traffic violations and accidents had no significant effect on the people's age group (p > 0.05).Meanwhile, Table 5 shows the statistically significant difference between riding experience and accidents, where the people with 1-3 years (1.75 ± 1.215) of riding experience have a greater chance of causing or being in an accident than the other age groups, followed by the people who have 4-7 years of riding experience (1.64 ± 1.036).The other factors (traffic violations and level of injury) show that the results are not statistically significant.

Exploratory Factor Analysis
Exploratory factor analysis was performed with the data from 399 participants.Principal component analysis with rotation, or more precisely, varimax rotation with Kaiser normalization, was the technique we employed.The findings are summarized in Table 6, which explains about 51.50% of the total patterns we observed.Only factor loading that is stronger than 0.40 is discussed.We identified four main categories: speed violations, control errors, traffic errors, and safety.The items are listed from the most important to the least important based on their contribution.The Kaiser-Meyer-Olkin measure came out as 0.967, which means that our sample was good for this type of analysis.Another test we performed, called Bartlett's test of sphericity, showed that the data were appropriate for this type of analysis (χ 2 = 15294.799;df = 741, p < 0.000).created by modifying the free parameters until the model fit indices attained levels that were acceptable in order to obtain a satisfactory fit.Table 6 shows the CFA assessment methodology.The fitting indices for the modified model are shown in Table 7.
Overall, the results of the CFA demonstrate a well-fitted model, and the standardized estimates, ranging from 0.540 to 0.907, indicate robust relationships between the variables under consideration.To further evaluate the internal consistency of the constructs, we utilized the composite reliability index and average variance extracted, with recommended thresholds of CR ≥ 0.7 and AVE ≥ 0.5 [46].The relationships between variables were examined through correlation testing at a significance level of p < 0.01.The results of the correlations are presented in Table 8.

Validity and Reliability
We examined the precision and dependability of the measurement model and evaluated the correctness of the indicators by utilizing Cronbach's alpha.It has been suggested that a Cronbach's alpha value exceeding 0.6 is the minimum threshold for establishing the reliability of a self-report questionnaire [42][43][44].The reliability analysis yielded Cronbach's alpha values between 0.972 and 0.699 for the factors of speed violations, control errors, traffic errors, and safety; meanwhile, the cumulative of the variance explained greater than 50 percent is also shown in Table 9.Additionally, the AVE values obtained for these factors ranged between 0.436 and 0.686 while CR values obtained ranged between 0.697 and 0972 as shown in Table 9.As a result, the AVE values for control error and safety fell below the recommended threshold.According to the criteria outlined by Fornell and Larcker, the convergent validity of the construct is supposed to be satisfactory when the AVE is less than 0.5 and the CR surpasses 0.6 [53].Thus, the measurement model we used in our study is consistent and reliable and supports the validity of our research.

Discussion and Conclusions
In this research, our objective was to develop a model for assessing motorcycle riding behaviors specific to the Thai population.We achieved this by adapting the motorcycle rider behavior questionnaire (MRBQ) through exploratory factor analysis.Additionally, we aimed to validate this measurement model using confirmatory factor analysis.This validation approach had been previously employed in studies by [4,[17][18][19]21].
Furthermore, our aim extended to establishing guidelines for a self-assessment report concerning motorcycle riding behaviors among Thai individuals.To achieve this, we utilized MRBQ, a tool designed to study motorcycle riding behaviors.We applied this tool to Thai riders, incorporating the original 24 items from Elliott et al.'s [4] work in 2006, as well as 15 additional items related to unsafe riding behaviors.Utilizing factor analysis, we separated these behaviors into four different factors: speed violations, control errors, traffic errors, and safety.
It is important to note that our study specifically focused on collecting data from middle-aged individuals in Thailand.Our findings in terms of factors closely resembled those of Sakashita et al. [21].When analyzing these factors, we observed that speed violations exhibited the highest loading factor value.This category contained actions such as excessive speed, breaking speed limits, riding under the influence, and engaging in racing on roads.Regarding control errors, our study identified frequently reported behaviors of Thai respondents, such as using electronic devices while riding, failing to stay in the correct lane, and riding with just one hand.These behaviors are often driven by time constraints and a reliance on convenience devices.In terms of traffic errors, a combination of behaviors occurred, including disregarding "GIVE WAY" signs on narrow roads and failing to yield to pedestrians at zebra crossings.The agility of motorcycles often leads riders to weave through traffic, sometimes resulting in sudden encounters with obstacles, particularly at intersections.A significant portion of accidents at traffic light junctions involved motorcycles crashing with other vehicles.Lastly, our observations regarding safety indicated a concerning trend: a notable portion of Thai riders do not wear helmets while operating motorcycles.This behavior can significantly increase the quantity of injuries in the event of an accident.

Limitation
The study faces several limitations that should be addressed in future research.Firstly, despite the inclusion of a substantial sample size of 399 middle-aged motorcycle riders in Thailand, the study's sample may not fully capture the diversity of motorcycle riders in the country, thus potentially limiting the generalizability of findings to the broader population of riders.Secondly, reliance on self-reported data introduces the possibility of self-reporting bias where the participants may underreport certain unsafe behaviors or overemphasize adherence to safety protocols, thereby impacting the accuracy of the findings.Lastly, while survey-based methods were primarily utilized for data collection, the incorporation of complementary methods such as observational studies or interviews could offer deeper insights into motorcycle riding behaviors, providing a more comprehensive understanding of the factors influencing rider behavior.

Future Work
Several avenues for future research could enhance our understanding of motorcycle riding behaviors and contribute to the development of effective safety initiatives.Firstly, conducting longitudinal studies to track changes in motorcycle riding behaviors among different age groups over time would provide insights into the development of riding habits and the effectiveness of safety interventions.Secondly, comparative analyses across different regions or countries could be employed to identify cultural and environmental factors influencing riding habits, thereby informing the development of tailored safety initiatives.Moreover, enhancing quantitative data with qualitative research techniques like interviews or focus groups would provide more profound understandings of motorcycle riding habits, adding context and subtlety to numerical results.Lastly, investigating the incorporation of technology, such as smartphone applications or wearable devices, for real-time monitoring and enhancement of motorcycle riding behaviors is warranted.These research directions hold potential for advancing our understanding of motorcycle safety and contributing to the reduction in accidents and injuries among riders.Table A2.Description of the questionnaire data.

Table 1 .
Related works and factors in previous motorcycle riding behavior research.

Table 3 .
Analysis of riding behavior versus age in years of motorcyclist (n = 399).

Table 4 .
ANOVA testing of risk and age groups (n = 399).

Table 5 .
ANOVA testing of risk and riding experience (n = 399).

Table 9 .
Construct validity and reliability result.

Table A1 .
Cont.X34When waiting to turn left on the main road, focusing so much on the main traffic that you almost hit the vehicle in front of you.+ − X35 Missing the "Give Way" signs and narrowly avoiding a collision with traffic that has the right of way.+ −X36Being distracted or lost in thought, and suddenly realizing that the vehicle in front of you has slowed down, forcing you to brake suddenly to avoid a crash.